SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Multi-Faceted Bug Reproduction Tests
arXiv:2607.00990v1 Announce Type: cross Abstract: Large language model (LLM)-based software engineering agents are increasingly developed to resolve software issues by generating patches from issue reports and code repositories. Bug reproduction tests (BRTs) are an important building block for such agents and have been shown useful for patch validation. However, it remains unclear whether BRTs can also help the more central stage of patch generation. We first conduct a preliminary study and find that directly using advanced BRT generators to guide patch generation is not beneficial: fail-to-fa
This paper addresses a fundamental challenge in the current development of LLM-based software engineering agents, which are rapidly advancing but still face limitations in complex task performance.
Improving the guidance of AI agents during critical stages like patch generation directly impacts their effectiveness and could significantly accelerate software development and maintenance cycles.
This research explores a new method to enhance the patch generation capabilities of LLM-based software engineering agents by using runtime diagnosis from bug reproduction tests, moving beyond mere validation.
- · Software Engineering Agents
- · Developers
- · AI/ML Research Institutions
- · Manual Software Debugging
LLM-based agents will become more autonomous and effective at identifying and fixing software bugs.
The speed and quality of software development will increase, leading to faster innovation cycles across industries.
A highly automated software development pipeline could reduce demand for some human coding tasks, shifting roles towards agent supervision and higher-level architecture.
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Read at arXiv cs.AI